





'''simple docstring'''

import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class __a			:





   def __init__(       self					:      Dict					,lowerCamelCase					:      str					,lowerCamelCase					:      Dict=13					,lowerCamelCase					:      Dict=7					,lowerCamelCase					:      int=True					,lowerCamelCase					:      Tuple=True					,lowerCamelCase					:      List[Any]=False					,lowerCamelCase					:      List[str]=True					,lowerCamelCase					:      List[str]=99					,lowerCamelCase					:      Optional[int]=32					,lowerCamelCase					:      Any=5					,lowerCamelCase					:      Any=4					,lowerCamelCase					:      Union[str, Any]=37					,lowerCamelCase					:      Optional[int]="gelu"					,lowerCamelCase					:      Tuple=0.1					,lowerCamelCase					:      Union[str, Any]=0.1					,lowerCamelCase					:      Optional[int]=512					,lowerCamelCase					:      Dict=16					,lowerCamelCase					:      Dict=2					,lowerCamelCase					:      str=0.02					,lowerCamelCase					:      Optional[int]=3					,lowerCamelCase					:      str=4					,lowerCamelCase					:      Tuple=None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							seq_length
      __SCREAMING_SNAKE_CASE   				=							is_training
      __SCREAMING_SNAKE_CASE   				=							use_input_mask
      __SCREAMING_SNAKE_CASE   				=							use_token_type_ids
      __SCREAMING_SNAKE_CASE   				=							use_labels
      __SCREAMING_SNAKE_CASE   				=							vocab_size
      __SCREAMING_SNAKE_CASE   				=							hidden_size
      __SCREAMING_SNAKE_CASE   				=							num_hidden_layers
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							intermediate_size
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							max_position_embeddings
      __SCREAMING_SNAKE_CASE   				=							type_vocab_size
      __SCREAMING_SNAKE_CASE   				=							type_sequence_label_size
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							num_labels
      __SCREAMING_SNAKE_CASE   				=							num_choices
      __SCREAMING_SNAKE_CASE   				=							scope





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_input_mask:
         __SCREAMING_SNAKE_CASE   				=							random_attention_mask([self.batch_size, self.seq_length]     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_token_type_ids:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.seq_length]					,self.type_vocab_size     )

      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_labels:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.type_sequence_label_size     )
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.seq_length]					,self.num_labels     )
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.num_choices     )

      __SCREAMING_SNAKE_CASE   				=							self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      return LlamaConfig(
          vocab_size=self.vocab_size					,hidden_size=self.hidden_size					,num_hidden_layers=self.num_hidden_layers					,num_attention_heads=self.num_attention_heads					,intermediate_size=self.intermediate_size					,hidden_act=self.hidden_act					,hidden_dropout_prob=self.hidden_dropout_prob					,attention_probs_dropout_prob=self.attention_probs_dropout_prob					,max_position_embeddings=self.max_position_embeddings					,type_vocab_size=self.type_vocab_size					,is_decoder=lowerCamelCase					,initializer_range=self.initializer_range					,)





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      str					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							LlamaModel(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,attention_mask=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)     )





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Any					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      List[str]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							LlamaModel(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,encoder_attention_mask=lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,attention_mask=lowerCamelCase     )
      self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)     )





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Any					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Any					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[str]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,attention_mask=lowerCamelCase					,labels=lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Any					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      str					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[Any]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()

      # first forward pass
      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,encoder_attention_mask=lowerCamelCase					,use_cache=lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      __SCREAMING_SNAKE_CASE   				=							ids_tensor((self.batch_size, 3)					,config.vocab_size     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor((self.batch_size, 3)					,vocab_size=2     )

      # append to next input_ids and
      __SCREAMING_SNAKE_CASE   				=							torch.cat([input_ids, next_tokens]					,dim=-1     )
      __SCREAMING_SNAKE_CASE   				=							torch.cat([input_mask, next_mask]					,dim=-1     )

      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,encoder_attention_mask=lowerCamelCase					,output_hidden_states=lowerCamelCase					,)["""hidden_states"""][0]
      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,encoder_attention_mask=lowerCamelCase					,past_key_values=lowerCamelCase					,output_hidden_states=lowerCamelCase					,)["""hidden_states"""][0]

      # select random slice
      __SCREAMING_SNAKE_CASE   				=							ids_tensor((1,)					,output_from_past.shape[-1]     ).item()
      __SCREAMING_SNAKE_CASE   				=							output_from_no_past[:, -3:, random_slice_idx].detach()
      __SCREAMING_SNAKE_CASE   				=							output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(lowerCamelCase					,lowerCamelCase					,atol=1E-3     )     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.prepare_config_and_inputs()
      (
          (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  
      )   				=							config_and_inputs
      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": input_ids, """attention_mask""": input_mask}
      return config, inputs_dict







@require_torch
class __a			( _snake_case, _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			Optional[Any]						   =						(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
   __UpperCamelCase :			int						   =						(LlamaForCausalLM,) if is_torch_available() else ()
   __UpperCamelCase :			Union[str, Any]						   =						(
       {
           'feature-extraction': LlamaModel,
           'text-classification': LlamaForSequenceClassification,
           'text-generation': LlamaForCausalLM,
           'zero-shot': LlamaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCamelCase :			List[str]						   =						False
   __UpperCamelCase :			Tuple						   =						False





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							LlamaModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(self					,config_class=lowerCamelCase					,hidden_size=37     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         __SCREAMING_SNAKE_CASE   				=							type
         self.model_tester.create_and_check_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							3
      __SCREAMING_SNAKE_CASE   				=							input_dict["""input_ids"""]
      __SCREAMING_SNAKE_CASE   				=							input_ids.ne(1     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.model_tester.batch_size]					,self.model_tester.type_sequence_label_size     )
      __SCREAMING_SNAKE_CASE   				=							LlamaForSequenceClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,attention_mask=lowerCamelCase					,labels=lowerCamelCase     )
      self.assertEqual(result.logits.shape					,(self.model_tester.batch_size, self.model_tester.num_labels)     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							3
      __SCREAMING_SNAKE_CASE   				=							"""single_label_classification"""
      __SCREAMING_SNAKE_CASE   				=							input_dict["""input_ids"""]
      __SCREAMING_SNAKE_CASE   				=							input_ids.ne(1     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.model_tester.batch_size]					,self.model_tester.type_sequence_label_size     )
      __SCREAMING_SNAKE_CASE   				=							LlamaForSequenceClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,attention_mask=lowerCamelCase					,labels=lowerCamelCase     )
      self.assertEqual(result.logits.shape					,(self.model_tester.batch_size, self.model_tester.num_labels)     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							3
      __SCREAMING_SNAKE_CASE   				=							"""multi_label_classification"""
      __SCREAMING_SNAKE_CASE   				=							input_dict["""input_ids"""]
      __SCREAMING_SNAKE_CASE   				=							input_ids.ne(1     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor(
          [self.model_tester.batch_size, config.num_labels]					,self.model_tester.type_sequence_label_size     ).to(torch.float     )
      __SCREAMING_SNAKE_CASE   				=							LlamaForSequenceClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,attention_mask=lowerCamelCase					,labels=lowerCamelCase     )
      self.assertEqual(result.logits.shape					,(self.model_tester.batch_size, self.model_tester.num_labels)     )





   @unittest.skip("""LLaMA buffers include complex numbers, which breaks this test"""     )
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      pass





   @parameterized.expand([("""linear""",), ("""dynamic""",)]     )
   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							ids_tensor([1, 10]					,config.vocab_size     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor([1, int(config.max_position_embeddings * 1.5     )]					,config.vocab_size     )

      set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
      __SCREAMING_SNAKE_CASE   				=							LlamaModel(lowerCamelCase     )
      original_model.to(lowerCamelCase     )
      original_model.eval()
      __SCREAMING_SNAKE_CASE   				=							original_model(lowerCamelCase     ).last_hidden_state
      __SCREAMING_SNAKE_CASE   				=							original_model(lowerCamelCase     ).last_hidden_state

      set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
      __SCREAMING_SNAKE_CASE   				=							{"""type""": scaling_type, """factor""": 10.0}
      __SCREAMING_SNAKE_CASE   				=							LlamaModel(lowerCamelCase     )
      scaled_model.to(lowerCamelCase     )
      scaled_model.eval()
      __SCREAMING_SNAKE_CASE   				=							scaled_model(lowerCamelCase     ).last_hidden_state
      __SCREAMING_SNAKE_CASE   				=							scaled_model(lowerCamelCase     ).last_hidden_state

      # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
      # maximum sequence length, so the outputs for the short input should match.
      if scaling_type == "dynamic":
         self.assertTrue(torch.allclose(lowerCamelCase					,lowerCamelCase					,atol=1E-5     )     )
      else:
         self.assertFalse(torch.allclose(lowerCamelCase					,lowerCamelCase					,atol=1E-5     )     )

      # The output should be different for long inputs
      self.assertFalse(torch.allclose(lowerCamelCase					,lowerCamelCase					,atol=1E-5     )     )







@require_torch
class __a			( unittest.TestCase      ):





   @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""     )
   @slow
   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[1, 306, 4658, 278, 6593, 310, 2834, 338]
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""					,device_map="""auto"""     )
      __SCREAMING_SNAKE_CASE   				=							model(torch.tensor([input_ids]     )     )
      # Expected mean on dim = -1
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]     )
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]     )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :30]					,lowerCamelCase					,atol=1E-5					,rtol=1E-5     )





   @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""     )
   @slow
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[1, 306, 4658, 278, 6593, 310, 2834, 338]
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""					,device_map="""auto"""     )
      __SCREAMING_SNAKE_CASE   				=							model(torch.tensor(lowerCamelCase     )     )
      # Expected mean on dim = -1
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]     )
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]     )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :30]					,lowerCamelCase					,atol=1E-5					,rtol=1E-5     )





   @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""     )
   @slow
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[1, 306, 4658, 278, 6593, 310, 2834, 338]
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""					,device_map="""auto"""     )
      __SCREAMING_SNAKE_CASE   				=							model(torch.tensor(lowerCamelCase     )     )
      # Expected mean on dim = -1
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]     )
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]     )
      # fmt: on
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )





   @unittest.skip(
       """Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"""     )
   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[1, 306, 4658, 278, 6593, 310, 2834, 338]
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""					,device_map="""auto"""     )
      __SCREAMING_SNAKE_CASE   				=							model(torch.tensor(lowerCamelCase     )     )

      __SCREAMING_SNAKE_CASE   				=							torch.tensor(
          [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]					,dtype=torch.floataa     )
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )
      # fmt: off
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]     )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :30]					,lowerCamelCase					,atol=1E-5					,rtol=1E-5     )





   @unittest.skip("""Model is curently gated"""     )
   @slow
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
      __SCREAMING_SNAKE_CASE   				=							"""Simply put, the theory of relativity states that """
      __SCREAMING_SNAKE_CASE   				=							LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""     )
      __SCREAMING_SNAKE_CASE   				=							tokenizer.encode(lowerCamelCase					,return_tensors="""pt"""     )
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained(
          """meta-llama/Llama-2-13b-chat-hf"""					,device_map="""sequential"""					,use_safetensors=lowerCamelCase     )

      # greedy generation outputs
      __SCREAMING_SNAKE_CASE   				=							model.generate(lowerCamelCase					,max_new_tokens=64					,top_p=lowerCamelCase					,temperature=1					,do_sample=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							tokenizer.decode(generated_ids[0]					,skip_special_tokens=lowerCamelCase     )
      self.assertEqual(lowerCamelCase					,lowerCamelCase     )











'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class  __lowercase    (	__magic_name__							,				__magic_name__							,				__magic_name__							,				unittest.TestCase			):
   _a    						=   StableUnCLIPImgaImgPipeline
   _a    						=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
   _a    						=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _a    						=   frozenset(
       []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   _a    						=   frozenset([]			)







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       32
     __a								=       embedder_hidden_size

     # image encoding components

     __a								=       CLIPImageProcessor(crop_size=32   ,				size=32   )

     torch.manual_seed(0   )
     __a								=       CLIPVisionModelWithProjection(
         CLIPVisionConfig(
             hidden_size=UpperCamelCase   ,				projection_dim=UpperCamelCase   ,				num_hidden_layers=5   ,				num_attention_heads=4   ,				image_size=32   ,				intermediate_size=37   ,				patch_size=1   ,				)   )

     # regular denoising components

     torch.manual_seed(0   )
     __a								=       StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase   )
     __a								=       DDPMScheduler(beta_schedule='squaredcos_cap_v2'   )

     torch.manual_seed(0   )
     __a								=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

     torch.manual_seed(0   )
     __a								=       CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=UpperCamelCase   ,				projection_dim=32   ,				intermediate_size=37   ,				layer_norm_eps=1e-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)   )

     torch.manual_seed(0   )
     __a								=       UNetaDConditionModel(
         sample_size=32   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')   ,				up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')   ,				block_out_channels=(32, 64)   ,				attention_head_dim=(2, 4)   ,				class_embed_type='projection'   ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2   ,				cross_attention_dim=UpperCamelCase   ,				layers_per_block=1   ,				upcast_attention=UpperCamelCase   ,				use_linear_projection=UpperCamelCase   ,				)

     torch.manual_seed(0   )
     __a								=       DDIMScheduler(
         beta_schedule='scaled_linear'   ,				beta_start=0.00_085   ,				beta_end=0.012   ,				prediction_type='v_prediction'   ,				set_alpha_to_one=UpperCamelCase   ,				steps_offset=1   ,				)

     torch.manual_seed(0   )
     __a								=       AutoencoderKL()

     __a								=       {
         # image encoding components
         'feature_extractor': feature_extractor,
         'image_encoder': image_encoder.eval(),
         # image noising components
         'image_normalizer': image_normalizer.eval(),
         'image_noising_scheduler': image_noising_scheduler,
         # regular denoising components
         'tokenizer': tokenizer,
         'text_encoder': text_encoder.eval(),
         'unet': unet.eval(),
         'scheduler': scheduler,
         'vae': vae.eval(),
     }

     return components







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=0   ,				UpperCamelCase=True   )					->     Dict:
     if str(UpperCamelCase   ).startswith('mps'   ):
       __a								=       torch.manual_seed(UpperCamelCase   )
     else:
       __a								=       torch.Generator(device=UpperCamelCase   ).manual_seed(UpperCamelCase   )

     __a								=       floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(UpperCamelCase   )   ).to(UpperCamelCase   )

     if pil_image:
       __a								=       input_image * 0.5 + 0.5
       __a								=       input_image.clamp(0   ,				1   )
       __a								=       input_image.cpu().permute(0   ,				2   ,				3   ,				1   ).float().numpy()
       __a								=       DiffusionPipeline.numpy_to_pil(UpperCamelCase   )[0]

     return {
         "prompt": "An anime racoon running a marathon",
         "image": input_image,
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "np",
     }







   @skip_mps
   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       'cpu'  # ensure determinism for the device-dependent torch.Generator
     __a								=       self.get_dummy_components()
     __a								=       StableUnCLIPImgaImgPipeline(**UpperCamelCase   )
     __a								=       sd_pipe.to(UpperCamelCase   )
     sd_pipe.set_progress_bar_config(disable=UpperCamelCase   )

     __a								=       self.get_dummy_inputs(UpperCamelCase   )
     inputs.update({'image_embeds': None}   )
     __a								=       sd_pipe(**UpperCamelCase   ).images
     __a								=       image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     __a								=       np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       torch_device in ['cpu', 'mps']

     self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       torch_device in ['cpu', 'mps']

     self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase   )







   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()   ,				reason='XFormers attention is only available with CUDA and `xformers` installed'   ,				)
   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     self._test_xformers_attention_forwardGenerator_pass(test_max_difference=UpperCamelCase   )





@slow
@require_torch_gpu
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     __a								=       load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'   )

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-l-img2img'   ,				torch_dtype=torch.floataa   )
     pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       torch.Generator(device='cpu'   ).manual_seed(0   )
     __a								=       pipe(UpperCamelCase   ,				'anime turle'   ,				generator=UpperCamelCase   ,				output_type='np'   )

     __a								=       output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     __a								=       load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'   )

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-h-img2img'   ,				torch_dtype=torch.floataa   )
     pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       torch.Generator(device='cpu'   ).manual_seed(0   )
     __a								=       pipe(UpperCamelCase   ,				'anime turle'   ,				generator=UpperCamelCase   ,				output_type='np'   )

     __a								=       output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-h-img2img'   ,				torch_dtype=torch.floataa   )
     __a								=       pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       pipe(
         UpperCamelCase   ,				'anime turtle'   ,				num_inference_steps=2   ,				output_type='np'   ,				)

     __a								=       torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9



